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Linguistic control

We have developed original concept of fuzzy control. Our idea goes back to the roots of fuzzy control: The process is a black box; we know, how to control it but the process itself is not known to us. It may also happen that mathematical description of the process is too complicated to be solved, or it is very expensive or even dangerous to make experiments so that the mathematical description could be obtained.

It is usual that we are able to describe the control strategy in natural language. Our approach enables using natural language directly without thinking of the way how is the proper fuzzy control realized. Thus, the user should not think of shapes of fuzzy sets, used operations, inference engine, etc., as it is otherwise usual in fuzzy control widely considered elsewhere. We speak about linguistically oriented fuzzy control and the corresponding system is called Linguistic Fuzzy Logic Controller (LFLC). We may also see LFLC as if “partner” whose task is to realize control according to instruction given in natural language. Note that such situation is not so unusual; remember, e.g., instruction given by teacher in car-driving school.

Using the software systems LFLCSim and LFLC2000 we developed several tens of simulations of control of various kinds of processes. The fuzzy PI, PD, and PID controllers have been implemented in them. A real application of linguistically oriented fuzzy control is described in [1].

Simple PI control

The following demonstration shows simulation of fuzzy control in a closed feedback loop. The control is good at any Set point. The control can be adapted to make it very precise. The same fuzzy control is repeated together with randomly generated disturbance on the input. The demonstration also shows detailed inspection of the control process.

There exists general linguistic description for PI fuzzy control of wide range of processes.

No.

E

dE

dU

No.

E

dE

dU

1.

Ze

Ze

Ze

19.

-Me

+Sm

-ExSm

2.

+Bi

+NoZe

+Bi

20.

+Sm

+NoSm

+Me

3.

-Bi

-NoZe

-Bi

21.

-Sm

-NoSm

-Me

4.

+Bi

-NoSm

Ze

22.

+Sm

-NoSm

-VeSm

5.

-Bi

+NoSm

Ze

23.

-Sm

+NoSm

+VeSm

6.

+Bi

RoZe

+Me

24.

+Sm

RoZe

+VeSm

7.

-Bi

RoZe

-Me

25.

-Sm

RoZe

-VeSm

8.

+Bi

-Sm

+Me

26.

+Sm

+Sm

+Sm

9.

-Bi

+Sm

-Me

27.

+Sm

-Sm

+ExSm

10.

+Me

+NoSm

+Bi

28.

-Sm

+Sm

-ExSm

11.

-Me

-NoSm

-Bi

29.

-Sm

-Sm

-Sm

12.

+Me

-NoSm

-VeSm

30.

+VeSm

-Sm

-Sm

13.

-Me

+NoSm

+VeSm

31.

-VeSm

+Sm

+Sm

14.

+Me

RoZe

+Me

32.

+VeSm

-VeSm

Ze

15.

-Me

RoZe

-Me

33.

-VeSm

+VeSm

Ze

16.

+Me

+Sm

+Me

34.

RoZe

+VeSm

+ExSm

17.

-Me

-Sm

-Me

35.

RoZe

-VeSm

-ExSm

18.

+Me

-Sm

+ExSm

The following set of demonstrations shows the use of this linguistic description in the control of 6 different processes. The description is in all cases the same, only linguistic context (scaling) of the variables had to be set.

We have developed also a powerful learning ability of LFLC. The following demonstration shows first successful manual control of a given process. The controlled process is monitored and on the basis of the obtained data a linguistic description is learned. The latter is then used for control of the same process.